arXiv subjects

Martin Mönnigmann

Publications and source records attributed to Martin Mönnigmann.

At least 19 recordsLinked to original sources

arXiv2026

Dynamical principles of habituation across substrates and scales

Habituation is a basic form of learning in which a system's response to repeated stimulation progressively diminishes but eventually recovers when the stimulus is withheld. Long studied in animals, it has increasingly been observed in unicellular organisms and non-living devices such as electronic circuits and neuromorphic materials, suggesting underlying dynamical principles that recur across domains. This review asks what those principles are: given qualitative constraints imposed by habituation on a system's response, what is the minimal dynamical structure that satisfies them? We formalize the classical hallmarks of habituation as behavioral constraints on input--output behavior, show that linear time-invariant systems are structurally incompatible with these constraints, and construct nonlinear motifs---linear fading-memory dynamics composed with static nonlinearities---that exhibit the hallmarks across diverse settings. We relate these motifs to models of specific biological systems and to physical and algorithmic realizations, from analog circuits to transient computation in machine learning.

eess.SY↗

arXiv2026

LIVE-RIS: Real-Time In-Flight Actuation of UAV-Mounted RIS

Reconfigurable intelligent surfaces (RIS) are emerging as a key technology for sixth-generation (6G) wireless networks due to their ability to dynamically control the propagation environment. To ensure favorable Line-of-Sight (LoS) conditions in real-world applications, the RIS is mounted on an unmanned aerial vehicle (UAV). While the potential of UAV-mounted RIS has been extensively studied in theoretical works, experimental validation with real-world data remains limited. Such validation is particularly important, as UAV motion and disturbances may degrade the performance of the RIS-enabled link. In this paper, we present the first fully functional, real-time capable UAV-mounted RIS prototype and validate its performance through experimental measurements under realistic disturbances and hardware constraints. We show that the RIS pose can be predicted based on the UAV's extended Kalman filter (EKF) and onboard sensors. By utilizing this estimation, we demonstrate that the RIS can be reconfigured in real time, effectively mitigating disturbance effects and preserving the performance gains of the RIS-enabled link. Furthermore, we systematically evaluate different deployment locations to provide insights into RIS performance in real-world scenarios.

eess.SP↗

arXiv2026

Design and Deployment Guidelines for UAV-Mounted RIS Under Position Uncertainty

UAV-mounted reconfigurable intelligent surfaces (RIS) are a promising enabler for 6G networks, offering dynamic control of wireless propagation for coverage enhancement, integrated sensing and communication (ISAC), and localization. By exploiting UAV mobility, RIS can maintain favorable line-of-sight links, improving channel quality in dynamic environments. However, UAV positioning uncertainties introduce channel distortions that degrade RIS phase alignment and coherent combining. This work develops a GUM-based uncertainty propagation framework for UAV-mounted RIS channels, mapping UAV position uncertainty through the geometric Tx-RIS-Rx model into the complex cascaded channel. We derive a closed-form stochastic propagation model capturing nonlinear phase uncertainty effects and quantify their impact on channel coherence. The results show that phase uncertainty induces exponential coherence loss, dominating performance degradation. To characterize this transition, we introduce a performance-driven coherence threshold (PCT) that defines the boundary where incoherent combining results in a predetermined performance loss. Results based on analytical scaling laws and Monte Carlo simulations confirm the tightness of the PCT in accurately capturing the coherence transition. This validated threshold is then leveraged to derive optimal UAV-mounted RIS placement, revealing that realistic positioning conditions significantly deviate from the conventional RIS intuition, which typically favors placement close to either the transmitter or receiver.

eess.SP↗

arXiv2026

Learning Control-Affine Reduced-Order Models via Autoencoders

We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (AEs) to transform the high-dimensional states, and potentially the high-dimensional inputs, into reduced latent ones suitable for control-affine state-space dynamics. This is achieved by simultaneous training of the AE and the state-space model. In addition, we extend the discrete ROM formulation to a sequence-based model, which processes state and input histories to improve prediction accuracy while preserving the control-affine structure. We motivate our framework by applying feedback linearization to the derived models, and we present guidelines for its efficient use. The proposed framework is assessed on two numerical examples and its performance is compared to a baseline model, where the AE identifies a latent space with linear state-space dynamics. The assessment involves evaluating the prediction accuracy of the ROM on test data and its effectiveness in controlling the system to a desired state or trajectory.

math.DS↗

arXiv2025

Efficient Adjoint Petrov-Galerkin Reduced Order Models for fluid flows governed by the incompressible Navier-Stokes equations

This research paper investigates the Adjoint Petrov-Galerkin (APG) method for reduced order models (ROM) and fluid dynamics governed by the incompressible Navier-Stokes equations. The Adjoint Petrov-Galerkin ROM, derived using the Mori-Zwanzig formalism, demonstrates superior accuracy and stability compared to standard Galerkin ROMs. However, challenges arise due to the time invariance of the test basis vectors, resulting in high computational requirements. To address this, we introduce a new efficient Adjoint Petrov-Galerkin (eAPG) ROM formulation, extending its application to the incompressible Navier-Stokes equations by exploiting the polynomial structure inherent in these equations. The offline and online phases partition eliminates the need for repeated test basis vector evaluations. This improves computational efficiency in comparison to the general Adjoint Petrov-Galerkin ROM formulation. A novel approach to augmenting the memory length, a critical factor influencing the stability and accuracy of the APG-ROM, is introduced, employing a data-driven optimization. Numerical results for the 3D turbulent flow around a circular cylinder demonstrate the efficacy of the proposed approach. Error measures and computational cost evaluations, considering metrics such as floating point operations and simulation time, provide a comprehensive analysis.

eess.SY↗

arXiv2025

Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control

We propose a novel approach to solving input- and state-constrained parametric mixed-integer optimal control problems using Differentiable Predictive Control (DPC). Our approach follows the differentiable programming paradigm by learning an explicit neural policy that maps control parameters to integer- and continuous-valued decision variables. This policy is optimized via stochastic gradient descent by differentiating the quadratic model predictive control objective through the closed-loop finite-horizon response of the system dynamics. To handle integrality constraints, we incorporate three differentiable rounding strategies. The approach is evaluated on a conceptual thermal energy system, comparing its performance with the optimal solution for different lengths of the prediction horizon. The simulation results indicate that our self-supervised learning approach can achieve near-optimal control performance while significantly reducing inference time by avoiding online optimization, thus implying its potential for embedded deployment even on edge devices.

eess.SY↗

arXiv2025

Optimal Mode Decomposition for Control

We present an extension of optimal mode decomposition (OMD) for autonomous systems to systems with controls. The extension is developed along the same lines as the extension of dynamic mode decomposition (DMD) to DMD with control (DMDc). DMD identifies a linear dynamic system from high-dimensional snapshot data. DMD is often combined with a subsequent reduction by a projection to a truncated basis for the space spanned by the snapshots. In OMD, the identification and reduction are essentially integrated into a single optimization step, thus avoiding the somewhat adhoc decoupled, a posteriori reduction that is necessary if DMD is to be used for model reduction. DMD was devised for autonomous systems and later extended to DMD for systems with control inputs (DMDc). We present the analogous extension of OMD to OMDc, i.e. OMD for systems with control inputs. We illustrate the proposed method with an application to coupled diffusion-equations that model the drying of a wood chip. Reduced models of this type are required for the efficient simulation of industrial drying processes.

math.OC↗

arXiv2024

Uncertainty Propagation and Minimization for Channel Estimation in UAV-mounted RIS Systems

Reconfigurable Intelligent Surfaces (RIS) are emerging as a key technology for sixth-generation (6G) wireless networks, leveraging adjustable reflecting elements to dynamically control electromagnetic wave propagation and optimize wireless connectivity. By positioning the RIS on an unmanned aerial vehicle (UAV), it can maintain line-of-sight and proximity to both the transmitter and receiver, critical factors that mitigate path loss and enhance signal strength. The lightweight, power-efficient nature of RIS makes UAV integration feasible, yet the setup faces significant disturbances from UAV motion, which can degrade RIS alignment and link performance. In this study, we address these challenges using both experimental measurements and analytical methods. Using an extended Kalman filter (EKF), we estimate the UAV's orientation in real time during experimental flights to capture real disturbance effects. The resulting orientation uncertainty is then propagated to the RIS's channel estimates by applying the Guide to the Expression of Uncertainty in Measurement (GUM) framework as well as complex-valued propagation techniques to accurately assess and minimize the impact of UAV orientation uncertainties on RIS performance. This method enables us to systematically trace and quantify how orientation uncertainties affect channel gain and phase stability in real-time. Through numerical simulations, we find that the uncertainty of the RIS channel link is influenced by the RIS's configuration. Furthermore, our results demonstrate that the uncertainty area is most accurately represented by an annular section, enabling a 58% reduction in the uncertainty area while maintaining a 95% coverage probability.

eess.SY↗

arXiv2024

Minimal motifs for habituating systems

Habituation - a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld - is universally observed in living systems from animals to unicellular organisms. Despite its prevalence, generic mechanisms for this fundamental form of learning remain poorly defined. Drawing inspiration from prior work on systems that respond adaptively to step inputs, we study habituation from a nonlinear dynamics perspective. This approach enables us to formalize classical hallmarks of habituation that have been experimentally identified in diverse organisms and stimulus scenarios. We use this framework to investigate distinct dynamical circuits capable of habituation. In particular, we show that driven linear dynamics of a memory variable with static nonlinearities acting at the input and output can implement numerous hallmarks in a mathematically interpretable manner. This work establishes a foundation for understanding the dynamical substrates of this primitive learning behavior and offers a blueprint for the identification of habituating circuits in biological systems.

nlin.AO↗

arXiv2024

A minimal dynamical system and analog circuit for non-associative learning

Learning in living organisms is typically associated with networks of neurons. The use of large numbers of adjustable units has also been a crucial factor in the continued success of artificial neural networks. In light of the complexity of both living and artificial neural networks, it is surprising to see that very simple organisms -- even unicellular organisms that do not possess a nervous system -- are capable of certain forms of learning. Since in these cases learning may be implemented with much simpler structures than neural networks, it is natural to ask how simple the building blocks required for basic forms of learning may be. The purpose of this study is to discuss the simplest dynamical systems that model a fundamental form of non-associative learning, habituation, and to elucidate technical implementations of such systems, which may be used to implement non-associative learning in neuromorphic computing and related applications.

q-bio.NC↗

arXiv2024

Parametric 3D Convolutional Autoencoder for the Prediction of Flow Fields in a Bed Configuration of Hot Particles

The use of deep learning methods for modeling fluid flow has drawn a lot of attention in the past few years. In situations where conventional numerical approaches can be computationally expensive, these techniques have shown promise in offering accurate, rapid, and practical solutions for modeling complex fluid flow problems. The success of deep learning is often due to its ability to extract hidden patterns and features from the data, enabling the creation of data-driven reduced models that can capture the underlying physics of the domain. We present a data-driven reduced model for predicting flow fields in a bed configuration of hot particles. The reduced model consists of a parametric 3D convolutional autoencoder. The first part resolves the spatial and temporal dependencies present in the input sequence, while the second part of the architecture is responsible for predicting the solution at the subsequent timestep based on the information gathered from the preceding part. We also propose the utilization of a post-processing non-trainable output layer following the decoding path to incorporate the physical knowledge, e.g., no-slip condition, into the prediction. The evaluation of the reduced model for a bed configuration with variable particle temperature showed accurate results at a fraction of the computational cost required by traditional numerical simulation methods.

physics.flu-dyn↗

arXiv2023

A reduced model for particle calcination for use in DEM/CFD simulations

We treat the accurate simulation of the calcination reaction in particles, where the particles are large and, thus, the inner-particle processes must be resolved. Because these processes need to be described with coupled partial differential equations that must be solved numerically, the computation times for a single particle are too high for use in simulations that involve many particles. Simulations of this type arise when the Discrete Element Method (DEM) is combined with Computational Fluid Dynamics (CFD) to investigate industrial systems such as quick lime production in lime shaft kilns. We show that, based on proper orthogonal de-composition and Galerkin projection, reduced models can be derived for single particles that provide the same spatial and temporal resolution as the original PDE models at a considerably reduced computational cost. Replacing the finite-volume particle models with the reduced models results in an overall reduction of the reactor simulation time by about 60% for the simple example treated here.

physics.flu-dyn↗

arXiv2023

Estimating flow fields with Reduced Order Models

The estimation of fluid flows inside a centrifugal pump in realtime is a challenging task that cannot be achieved with long-established methods like CFD due to their computational demands. We use a projection-based reduced order model (ROM) instead. Based on this ROM, a realtime observer can be devised that estimates the temporally and spatially resolved velocity and pressure fields inside the pump. The entire fluid-solid domain is treated as a fluid in order to be able to consider moving rigid bodies in the reduction method. A greedy algorithm is introduced for finding suitable and as few measurement locations as possible. Robust observability is ensured with an extended Kalman filter, which is based on a time-variant observability matrix obtained from the nonlinear velocity ROM. We present the results of the velocity and pressure ROMs based on a unsteady Reynolds-averaged Navier-Stokes CFD simulation of a 2D centrifugal pump, as well as the results for the extended Kalman filter.

physics.flu-dyn↗

arXiv2023

Exploiting symmetries in active set enumeration for constrained linear-quadratic optimal control

This paper studies symmetric constrained linear-quadratic optimal control problems and their parametric solutions. The parametric solution of such a problem is a piecewise-affine feedback law that can be equivalently expressed as a set of active sets. We show symmetries of the optimal control problem entail symmetries of the active sets, which can be used to simplify finding the set of active sets considerably. Specifically, we improve a recently proposed method for the dynamic-programming-based enumeration of all active sets. The achieved reduction of the computational effort is illustrated with an example.

math.OC↗

arXiv2021

Teaching MPC: Which Way to the Promised Land?

Since the earliest conceptualizations by Lee and Markus, and Propoi in the 1960s, Model Predictive Control (MPC) has become a major success story of systems and control with respect to industrial impact and with respect to continued and wide-spread research interest. The field has evolved from conceptually simple linear-quadratic (convex) settings in discrete and continuous time to nonlinear and distributed settings including hybrid, stochastic, and infinite-dimensional systems. Put differently, essentially the entire spectrum of dynamic systems can be considered in the MPC framework with respect to both -- system theoretic analysis and tailored numerics. Moreover, recent developments in machine learning also leverage MPC concepts and learning-based and data-driven MPC have become highly active research areas. However, this evident and continued success renders it increasingly complex to live up to industrial expectations while enabling graduate students for state-of-the-art research in teaching MPC. Hence, this position paper attempts to trigger a discussion on teaching MPC. To lay the basis for a fruitful debate, we subsequently investigate the prospect of covering MPC in undergraduate courses; we comment on teaching textbooks; and we discuss the increasing complexity of research-oriented graduate teaching of~MPC.

math.OC↗

arXiv2021

Reducing the computational effort of min-max model predictive control with regional feedback laws

Recently, a regional MPC approach has been proposed that exploits the piecewise affine structure of the optimal solution (without computing the entire explicit solution before). Here, regional refers to the idea of using the affine feedback law that is optimal in a vicinity of the current state of operation, and therefore provides the optimal input signal without requiring to solve a QP. In the present paper, we apply the idea of regional MPC to min-max MPC problems. We show that the new robust approach can significantly reduce the number of QPs to be solved within min-max MPC resulting in a reduced overall computational effort. Moreover, we compare the performance of the new approach to an existing robust regional MPC approach using a numerical example with varying horizon. Finally, we provide a rule for choosing a suitable robust regional MPC approach based on the horizon.

math.OC↗

arXiv2021

Modeling pressure pulsation and backflow in progressing cavity pumps with deformable stator

This contribution studies the impact of the rotor-stator interaction in a single-stage progressing cavity pump on the flow rate and pressure. Specifically, we investigate the effect of the rotor movement on the sealings formed with deformable stators for various speeds and pressures. Sealings are reconstructed with the help of a geometric 3D model. We analyze the tangential and radial deviation of the rotor from its reference path and show that the radial deviation affects the flow rate, whereas the tangential deviation affects the pressure dynamics. The conjectures are confirmed with a laboratory test setup.

physics.flu-dyn↗

arXiv2020

Estimating load points of a motor-pump system using pressure and inverter drive data

We propose a novel method for the estimation of rotor position, speed, and torque of a motor-pump system consisting of a progressive cavity pump (PCP) driven by an induction motor which operates under V/f open-loop control. We compute the speed and rotor position of the PCP by applying a phase locked loop to the pressure signal at the pressure side of the pump. An extended Kalman filter is used to estimate the torque of the PCP based on the speed, effective value of the stator current of the induction motor and a nonlinear motor model. Furthermore, we derive a tractable condition under which the convergence of the observer is guaranteed. We use a laboratory experiment to verify our results.

eess.SY↗